





import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase   :							List[Any]    				=   logging.get_logger(__name__)




def _a   (    SCREAMING_SNAKE_CASE__     :    Union[str, Any]  )			->							Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__  :     Optional[int]           =		MobileNetVaConfig(layer_norm_eps=0.0_0_1  )

							if "_quant" in model_name:
														raise ValueError("Quantized models are not supported."  )

							SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"					, __snake_case  )
							if matches:
														SCREAMING_SNAKE_CASE__  :     Any           =		float(matches[1]  )
														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		int(matches[2]  )

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							SCREAMING_SNAKE_CASE__  :     str           =		10_01
							SCREAMING_SNAKE_CASE__  :     List[str]           =		"imagenet-1k-id2label.json"
							SCREAMING_SNAKE_CASE__  :     Any           =		"huggingface/label-files"
							SCREAMING_SNAKE_CASE__  :     Any           =		json.load(open(hf_hub_download(__snake_case					, __snake_case					, repo_type="dataset"  )					, "r"  )  )
							SCREAMING_SNAKE_CASE__  :     List[Any]           =		{int(__snake_case  ) + 1: v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE__  :     List[Any]           =		"background"
							SCREAMING_SNAKE_CASE__  :     str           =		idalabel
							SCREAMING_SNAKE_CASE__  :     Optional[int]           =		{v: k for k, v in idalabel.items()}

							return config




def _a   (    )			->							str:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__  :     int           =		"http://images.cocodataset.org/val2017/000000039769.jpg"
							SCREAMING_SNAKE_CASE__  :     List[Any]           =		Image.open(requests.get(__snake_case					, stream=__snake_case  ).raw  )
							return im




@torch.no_grad()
def _a   (    SCREAMING_SNAKE_CASE__     :    int					, SCREAMING_SNAKE_CASE__     :    Dict					, SCREAMING_SNAKE_CASE__     :    Any					, SCREAMING_SNAKE_CASE__     :    Optional[int]=False  )			->							List[Any]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		get_mobilenet_va_config(__snake_case  )

							# Load 🤗 model
							SCREAMING_SNAKE_CASE__  :     Dict           =		MobileNetVaForImageClassification(__snake_case  ).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(__snake_case					, __snake_case					, __snake_case  )

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		MobileNetVaImageProcessor(
							    crop_size={"width": config.image_size, "height": config.image_size}					, size={"shortest_edge": config.image_size + 32}					, )
							SCREAMING_SNAKE_CASE__  :     Dict           =		image_processor(images=prepare_img()					, return_tensors="pt"  )
							SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		model(**__snake_case  )
							SCREAMING_SNAKE_CASE__  :     List[str]           =		outputs.logits

							assert logits.shape == (1, 10_01)

							if model_name == "mobilenet_v1_1.0_224":
														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]  )
							elif model_name == "mobilenet_v1_0.75_192":
														SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]  )
							else:
														SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]					, __snake_case					, atol=1E-4  )

							Path(__snake_case  ).mkdir(exist_ok=__snake_case  )
							print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''  )
							model.save_pretrained(__snake_case  )
							print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
							image_processor.save_pretrained(__snake_case  )

							if push_to_hub:
														print("Pushing to the hub..."  )
														SCREAMING_SNAKE_CASE__  :     List[str]           =		"google/" + model_name
														image_processor.push_to_hub(__snake_case  )
														model.push_to_hub(__snake_case  )


if __name__ == "__main__":
		_lowerCamelCase   :							Optional[int]    				=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''mobilenet_v1_1.0_224''',
		    type=str,
		    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
		)
		parser.add_argument(
		    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		_lowerCamelCase   :							List[str]    				=   parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)














"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
 from .tokenization_big_bird import BigBirdTokenizer
else:
 snake_case : Tuple     		=		None

snake_case : Tuple     		=		logging.get_logger(__name__)
snake_case : List[Any]     		=		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

snake_case : Dict     		=		{
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case : int     		=		{
    """google/bigbird-roberta-base""": 4_0_9_6,
    """google/bigbird-roberta-large""": 4_0_9_6,
    """google/bigbird-base-trivia-itc""": 4_0_9_6,
}


snake_case : List[str]     		=		"""▁"""



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    __UpperCAmelCase           = VOCAB_FILES_NAMES
    __UpperCAmelCase           = PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase           = BigBirdTokenizer
    __UpperCAmelCase           = ["""input_ids""", """attention_mask"""]
    __UpperCAmelCase           = []







    def __init__(		self :	Optional[int]				,    UpperCamelCase_ :	int=None				,    UpperCamelCase_ :	Dict=None				,    UpperCamelCase_ :	int="<unk>"				,    UpperCamelCase_ :	Tuple="<s>"				,    UpperCamelCase_ :	int="</s>"				,    UpperCamelCase_ :	Union[str, Any]="<pad>"				,    UpperCamelCase_ :	Optional[Any]="[SEP]"				,    UpperCamelCase_ :	int="[MASK]"				,    UpperCamelCase_ :	Tuple="[CLS]"				,    **UpperCamelCase_ :	Optional[Any]				,    ):





           '''simple docstring'''

           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else bos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else eos_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else unk_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else pad_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else cls_token
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           __magic_name__      				=    AddedToken(UpperCamelCase_				,    lstrip=UpperCamelCase_				,    rstrip=UpperCamelCase_ ) if isinstance(UpperCamelCase_				,    UpperCamelCase_ ) else mask_token

           super().__init__(
               UpperCamelCase_				,    tokenizer_file=UpperCamelCase_				,    bos_token=UpperCamelCase_				,    eos_token=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    cls_token=UpperCamelCase_				,    mask_token=UpperCamelCase_				,    **UpperCamelCase_				,    )

           __magic_name__      				=    vocab_file
           __magic_name__      				=    False if not self.vocab_file else True







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]
           if token_ids_a is None:
                  return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None				,    UpperCamelCase_ :	bool = False ):





           '''simple docstring'''

           if already_has_special_tokens:
                  if token_ids_a is not None:
                         raise ValueError(
                             'You should not supply a second sequence if the provided sequence of '
                             'ids is already formatted with special tokens for the model.' )
                  return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

           if token_ids_a is None:
                  return [1] + ([0] * len(UpperCamelCase_ )) + [1]
           return [1] + ([0] * len(UpperCamelCase_ )) + [1] + ([0] * len(UpperCamelCase_ )) + [1]







    def 				a__			(		self :	Optional[Any]				,    UpperCamelCase_ :	List[int]				,    UpperCamelCase_ :	Optional[List[int]] = None ):





           '''simple docstring'''

           __magic_name__      				=    [self.sep_token_id]
           __magic_name__      				=    [self.cls_token_id]

           if token_ids_a is None:
                  return len(cls + token_ids_a + sep ) * [0]
           return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	str				,    UpperCamelCase_ :	Optional[str] = None ):





           '''simple docstring'''

           if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                      'tokenizer.' )

           if not os.path.isdir(UpperCamelCase_ ):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
                  return
           __magic_name__      				=    os.path.join(
               UpperCamelCase_				,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )

           if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase_ ):
                  copyfile(self.vocab_file				,    UpperCamelCase_ )

           return (out_vocab_file,)